Transforming food production and supply with OR/Analytics
Bibliographic record
Abstract
The International Transactions in Operational Research (ITOR) has been the flagship journal of IFORS since its launch in 1994.The journal has served the IFORS community for a number of years with publishing the proceedings of conferences and the biographies of key figures in the OR field, who now belong to the IFORS Hall of Fame (https://www.ifors.org/ifors-hall-of-fame/)Thescientific editorial world substantially evolved over time, furthermore the ITOR mission expanded and began to publish high-quality scientific papers, capable of attracting citations.The initial goal for ITOR was to be indexed and then to increase the value of the key indicator of the success of a journal, its impact factor.Thanks to the commitment of the Editor-in-Chief, Celso Ribeiro, and the editorial board of the journal, year after year, ITOR has improved its impact factor (current impact factor: 2.987 ) and has become one of the leading journals for the OR community. The Consortium of International Agricultural ResearchCentres (CGIAR) is a global partnership that plays a major role in improving agriculture in developing countries.CGIAR aims to reduce rural poverty, increase food security, improve human health/nutrition and the sustainable management of natural resources.Fifteen international research centres pursue these goals.CGIAR has set up a "Platform for Big Data in Agriculture", which seeks to stimulate innovations around big data that can transform farming in developing countries.Robin Lougee, coauthor of this article, was the founding Steering Committee Chair for the Platform.The International Maize and Wheat Improvement Centre (CIMMYT), a non-profit Mexico-based international organization, is a CGIAR centre that researches maize and wheat production systems in the developing world to improve, sustainably, their productivity and the livelihood of farmers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".